Development and validation of a model to predict cognitive impairment in traumatic brain injury patients: a prospective observational study

Background: Traumatic brain injury (TBI) is a significant public health issue worldwide that affects millions of people every year. Cognitive impairment is one of the most common long-term consequences of TBI, seriously affect the quality of life. We aimed to develop and validate a predictive model for cognitive impairment in TBI patients, with the goal of early identification and support for those at risk of developing cognitive impairment at the time of hospital admission. Methods: The training cohort included 234 TBI patients, all of whom were admitted to the Department of Neurosurgery at the Third Affiliated Hospital of Soochow University from May 2017 to April 2020. These patients were selected from our previously published studies. Baseline characteristics, medical history, clinical TBI characteristics, treatment details, and vital signs during hospitalization were screened via least absolute shrinkage and selection operator (LASSO) and logistic regression to construct a predictive net risk score. The derived score represents an estimate of the risk of developing cognitive impairment in patients with TBI. A nomogram was constructed, and its accuracy and predictive performance were evaluated with the area under the receiver operating characteristic curve (AUC), calibration curves, and clinical decision curves. For the validation cohort, data were prospectively collected from TBI patients admitted to the Department of Neurosurgery at the Third Affiliated Hospital of Soochow University from March 1, 2024 to August 30, 2024, according to the inclusion and exclusion criteria. This study is registered with the Chinese Clinical Trial Registry (ChiCTR) at http://www.chictr.org.cn/ (registration number: ChiCTR2400083495). Findings: The training cohort included 234 patients. The mean (standard deviation, SD) age of the patients in the cohort was 47.74 (17.89) years, and 184 patients (78.63%) were men. The validation cohort included 84 patients with a mean (SD) age of 48.44 (14.42) years, and 68 patients (80.95%) were men. Among the 48 potential predictors, the following 6 variables were significant independent predictive factors and were included in the net risk score: age (odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.03-1.08, P = 0.00), years of education (OR = 0.80, 95% CI: 0.70-0.93, P = 0.00), pulmonary infection status (OR = 4.64, 95% CI: 1.41-15.27, P = 0.01), epilepsy status (OR = 4.79, 95% CI: 1.09-21.13, P = 0.04), cerebrospinal fluid leakage status (OR = 5.57, 95% CI: 1.08-28.75, P = 0.04), and the Helsinki score (OR = 1.53, 95% CI: 1.28-1.83, P = 0.00). The AUC in the training cohort was 0.90, and the cut-off value was 0.71. The AUC in the validation cohort was 0.87, and the cut-off value was 0.63. The score was translated into an online risk calculator that is freely available to the public (https://yuanxiaofang.shinyapps.io/Predict_cognitive_impairment_in_TBI/). Interpretation: This model for predicting post-TBI cognitive impairment has potential value for facilitating early predictions by clinicians, aiding the early initiation of preventative interventions for cognitive impairment. Funding: This research was supported by Science and Technology Development Plan Project of ChangZhou (CJ20229036); Science and Technology Project of Changzhou Health Commission (QN202113).